During the last three decades, subsurface radar (SR) imaging has proven to be a reliable tool for imaging and sensing a number of scenarios. This imaging technology is based on the dielectric differences between the materials that form the scan region propagation medium and the targets present inside it. In recent years, cylindrical scan trajectories have been used in some novel near field radar applications, such as Microwave Wood Inspection (MWI) and Breast Microwave Radar (BMR), because that this geometry better suits the shape of the scan regions in these situations. As the irradiation is performed along the scan trajectory, the target responses have different travel times, resulting in the formation of non-linear signatures. This makes it difficult to correctly determine the dimensions and locations of the different scattering structures present in the scan area. In order to properly visualize the targets reflections, the collected data must be focused.
A number of techniques have been proposed to reconstruct cylindrical near field radar images. In general, these methods can be classified as either time-shift techniques or wavefront reconstruction approaches. Time-shift techniques perform a shift-sum process over a set of regions of interest in the scan area. Two examples of this approach are the confocal mapping algorithm and the beamforming reconstruction method. Wavefront reconstruction approaches focus the data by processing the spectrum of the collected responses and transforming it from the spatial-temporal domain where it is originally acquired to the spatial domain where it will be displayed. Although each method has advantages and disadvantages of their own, time-shift techniques are simpler to implement and debug, while wavefront reconstruction approaches exhibit a higher Signal to Noise Ratio (SNR) and an increased focal quality.
Reference to the above can be found in the following references which are incorporated herein by reference:
i) Bryant, M. L., Gostin, L. L., Soumekh, M.: ‘3-D E-CSAR imaging of a T-72 tank and synthesis of its SAR reconstructions,’ IEEE Transactions on Aerospace and Electronic Systems, 2003, 39, (1), pp. 211-227;
ii) Fear, E. C., Stuchly, M. A.: ‘Microwave detection of breast cancer’ IEEE Transactions on Microwave Theory and Techniques, 2000, 48, (11), pp. 1854-1863;
iii) Kaestner, A. P., Baath, L. B.: ‘Microwave polarimetry tomography of wood’, IEEE Sensors Journal, 2005, 5, (2), pp. 209-215;
iv) Bond, E. J., Xu Li, Hagness, S. C., Van Veen, B. D.: ‘Microwave imaging via space-time beamforming for early detection of breast cancer’, IEEE Transactions on Antennas and Propagation, 2003, 51, (8), pp. 1690-1705;
v) Klemm, M., Craddock, I. J., Leendertz, J. A., Preece, A., Benjamin, R.: ‘Radar-Based Breast Cancer Detection Using a Hemispherical Antenna Array—Experimental Results’, IEEE Transactions on Antennas and Propagation, 2009, 57, (6), pp. 1692-1704; and
vi) Soumekh, M.: ‘Synthetic Aperture Radar Signal Processing with MATLAB Algorithms’ (Wiley-Interscience, New York City, N.Y., USA, 1999).
A problem with cylindrical radar reconstruction approaches is their execution time. Its computational complexity is in the order of O( n log n), where n is the input signal length, resulting in execution times in the order of minutes for each scan plane when executed in a conventional multicore Pentium CPU. This can be an issue in cylindrical near field radar applications, due to the fact that a low execution time is required to provide the high throughput that is needed.